Reading the bibliography…
2023
Adaptive Communications in Collaborative Perception with Domain Alignment for Autonomous Driving Hu, Senkang, Fang, Zhengru, An, Haonan et al.
Understand Collaborative perception among multiple connected and autonomous vehicles can greatly enhance perceptive capabilities by allowing vehicles to exchange supplementary information via communications.
Despite advances in previous approaches, challenges still remain due to channel variations and data heterogeneity among collaborative vehicles. To address these issues, we propose ACC-DA, a channel-aware collaborative perception framework to dynamically adjust the communication graph and minimize the average transmission delay while mitigating the side effects from the data heterogeneity. Our novelties lie in three aspects. Adaptive Communications in Collaborative Perception with Domain Alignment for Autonomous Driving · Around
Built on F. Yang, L. Herranz, J. van de Weijer, J. A. I. Guitián, A. López, and M. Mozerov, “Variable Rate Deep Image Compression with Modulated Autoencoder,”
Original
1912
Earlier work this paper cites.
Y.-C. Liu, J. Tian, C.-Y. Ma, N. Glaser, C.-W. Kuo, and Z. Kira, “Who2com: Collaborative Perception via Learnable Handshake Communication,” Mar. 2020, arXiv:2003.09575 [cs]
Original
2003
Earlier work this paper cites.
T.-H. Wang, S. Manivasagam, M. Liang, B. Yang, W. Zeng, J. Tu, and R. Urtasun, “V2VNet: Vehicle-to-Vehicle Communication for Joint Perception and Prediction,” Aug. 2020, arXiv:2008.07519 [cs]
Original
2008
Earlier work this paper cites.
L. Van der Maaten and G. Hinton, “Visualizing data using t-sne.”
Original
2008
Earlier work this paper cites.
H. Li, S. J. Pan, S. Wang, and A. C. Kot, “Domain Generalization with Adversarial Feature Learning,” in
2018
Earlier work this paper cites.
Similar Y.-C. Liu, J. Tian, N. Glaser, and Z. Kira, “When2com: Multi-Agent Perception via Communication Graph Grouping,” in
2020
Cited alongside, same era.
Y. Li, S. Ren, P. Wu, S. Chen, C. Feng, and W. Zhang, “Learning Distilled Collaboration Graph for Multi-Agent Perception,” in
2021
Cited alongside, same era.
R. Xu, Z. Tu, H. Xiang, W. Shao, B. Zhou, and J. Ma, “CoBEVT: Cooperative Bird’s Eye View Semantic Segmentation with Sparse Transformers,” Sep. 2022, arXiv:2207.02202 [cs]
Original
2022
Cited alongside, same era.
Y. Hu, S. Fang, Z. Lei, Y. Zhong, and S. Chen, “Where2comm: Communication-Efficient Collaborative Perception via Spatial Confidence Maps,” Sep. 2022, arXiv:2209.12836 [cs] version: 1
Original
2022
Cited alongside, same era.
R. Xu, H. Xiang, Z. Tu, X. Xia, M.-H. Yang, and J. Ma, “V2X-ViT: Vehicle-to-Everything Cooperative Perception with Vision Transformer,” Aug. 2022, arXiv:2203.10638 [cs]
Then K. Zhou, Z. Liu, Y. Qiao, T. Xiang, and C. C. Loy, “Domain Generalization: A Survey,”
Original
2022
Later among the works it cites.
R. Xu, H. Xiang, X. Xia, X. Han, J. Li, and J. Ma, “OPV2V: An Open Benchmark Dataset and Fusion Pipeline for Perception with Vehicle-to-Vehicle Communication,” Jun. 2022, arXiv:2109.07644 [cs]
Original
2022
Later among the works it cites.
K. Yang, D. Yang, J. Zhang, H. Wang, P. Sun, and L. Song, “What2comm: Towards Communication-efficient Collaborative Perception via Feature Decoupling,” 2023
2023
Closest in time.
S. Hu, Z. Fang, X. Chen, Y. Fang, and S. Kwong, “Towards Full-scene Domain Generalization in Multi-agent Collaborative Bird’s Eye View Segmentation for Connected and Autonomous Driving,” Nov. 2023, arXiv:2311.16754 [cs]
Original
2023
Closest in time.
S. Hu, Z. Fang, Y. Deng, X. Chen, and Y. Fang, “Collaborative Perception for Connected and Autonomous Driving: Challenges, Possible Solutions and Opportunities,” Jan. 2024, arXiv:2401.01544 [cs, eess]
Beyond the bibliography alphaXiv searches the wider corpus for related work and actual follow-ups.
Open on alphaXiv alphaXiv is searching for related work…
Cited alongside, same era.